Application of FGMM-MRF Hierarchical Model to Image Segmentation

In order to accurately describe the region structure of a higher-level label image, the interior region is modeled by isotropic Markov random field (MRF), while the boundary is modeled by anisotropic MRF. For lower-level gray image, the prior distribution of segmentation image is modeled by finite general mixture model (FGMM). According to the posterior distribution of the label image conditioned on the gray image corresponding to the conditional probability of FGMM-MRF model, the Bayes formulation and the local iterated conditional modes (ICM) optimization algorithm are adopted, and based on the MAP (maximum a posterior) criterion the image segmentation result is obtained. Numerical simulations demonstrate that the whole property and the boundary of image area show better vision effect with a test to synthetic image and real MR brain image.